Resting metabolic rate and activity: key components of seasonal variation in daily energy expenditure for the northern fur seal (<i>Callorhinus ursinus</i>)
Bibliographic record
Abstract
Seasonal changes in daily energy expenditure (DEE) and its key underlying components (costs of resting metabolic rate (RMR), thermoregulation, activity, and growth) were measured to determine seasonal energy requirements, bioenergetic priorities, and potential times of year when unpredicted episodes of nutritional stress would have their greatest effect on female northern fur seals (Callorhinus ursinus (L., 1758)). The mean (±SD) DEE of six captive juvenile female fur seals was 527.8 ± 65.7 kJ·kg−1·d−1 and fluctuated seasonally (lower during summer and winter, and up to 20% greater in spring and fall). RMR also changed significantly with season and was higher in the fall (potentially due to moulting or anticipated migratory activity). However, changes in RMR did not follow the same seasonal trend as those of DEE. The largest component of DEE was RMR (∼80%, on average), followed by the cost of activity (which may have driven some of the seasonal variations in DEE). In contrast, the energetic costs associated with growth and thermoregulation appeared negligible within the scope of overall energy expenditures. Elevated innate costs of RMR and higher growth rates in the fall and summer, respectively, suggest that inadequate nutrition could comparatively have greater negative effects on female fur seals during these seasons.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".